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Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.

An integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.

The study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.

The proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.

The current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.

The automobile industry is widely recognized as a key sector with strong industrial linkages that significantly contribute to economic growth. This chapter focuses on the export of energy vehicles in BRICS countries from 2015 to 2021, evaluating export sophistication using panel data. Given that industrial upgrading is a long-term and dynamic economic process, the study employs a dynamic panel model to analyze the relationship between energy vehicle export sophistication and industrial upgrading. The findings reveal a significant positive correlation between the export sophistication of energy vehicles and industrial upgrading in BRICS countries from an economic perspective. Export sophistication emerges as a critical internal factor in advancing the technological structure of export trade. Conversely, external factors such as research and development, foreign direct investment, and GDP growth show comparatively less influence. Therefore, the exportation of energy vehicles presents a valuable opportunity for driving industrial upgrading in BRICS countries.

Based on the reinforcement sensitivity theory (RST), this study aims to examine the key factors that influence users’ renewable energy (RE) continuance intention. More importantly, the mediating role of excitement and the moderating role of technology infrastructure (TEC) and anxiety in the renewable energy continuance intention (RECI) is explored.

A survey was conducted among RE users in Malaysia and the results of a questionnaire survey of 397 were analysed using partial least square structural equation modelling.

The study’s findings highlight the significant roles of excitement and anxiety in RE intentions, emphasizing the impact of psychological factors and government policy on RE adoption in Malaysia. It also proves the moderating effect of perceptual dimension attributed to TEC.

This study significantly advances the understanding of RECI, offering a replicable research framework that can be examined across regions and countries. Scholars can leverage the framework for further exploration, whereas managers should recognize reinforcement sensitivity influences on RECI and the impact of perceived technology infrastructural support.

Given that this study is a pioneer attempt to investigate the approach and inhibiting factors relating to RECI through the application of RST, It provides novel insight for future research on RE among researchers and practitioners, thereby contributing to the limited body of knowledge on the psychological dynamics of RECI of an emerging economy.

This study aims to address the critical need for innovation in the power grid sector, driven by global carbon reduction commitments. It highlights the pivotal role of critical success factors (CSFs) in enhancing system adaptability and environmental mitigation within India’s power industry.

This research is grounded on transition management theory to identify and validate the CSFs necessary to integrate energy storage systems (ESS). Here, exploratory factor analysis (EFA) and total interpretive structural modeling (TISM) are integrated to evaluate the model’s effectiveness in reducing CO₂ emissions while ensuring grid stability and flexibility.

The research develops a seven-level hierarchical model illustrating the interaction of ESS components for a stable power grid, clean energy and a profitable electric industry. It emphasizes the strategic significance of managing key factors to reduce CO₂ emissions and ensure grid stability. The study recommends continuous monitoring at tactical and operational levels to enhance overall performance.

The study provides policymakers with strategic insights for the successful implementation of smart grid initiatives, facilitating effective decarbonization of the electricity industry. Additionally, it offers a comprehensive framework for minimizing the environmental impact associated with electricity generation, thereby enhancing overall operational sustainability and efficiency.

The originality of this study lies in its integration of EFA and TISM for robust model assessment and the application of transition management theory to identify and validate CSFs in the integration of ESS. This approach offers a novel perspective on enhancing the sustainability and efficiency of power grids.

The balance between power supply and demand gets more challenging when electrical networks switch from centralized thermal power plants to distributed renewable energy sources for power generation. Such problems present a diverse set of challenges that require a solution through system and control methods. Hence, the purpose of this study is to understand the issues faced by each actor in the power sector’s supply chain, which would restrict the stability of the power supply and quality of service.

This study provides a conceptual model, soft system methodology (SSM), for power supply management or grid integration issues through the mapping of identified issues with their possible solutions.

This study offers an analysis that uses methods of problem structuring to construct the major issues and measure technological advancements in the energy sector. This research highlights the need to integrate energy storage systems with the grid for the effective operation of the system to manage various power supply issues.

SSM is used to establish a mechanism to manage grid integration problems by comparing established problems with their potential solutions. The resulting framework would help managers, researchers, policymakers, engineers and smart grid professionals to make the required and informed decisions on the management of grid integration issues and to form strategies fostering efficient and secure energy network.

The research is based on a conceptual framework for enhancing energy efficiency and integrated smart grid technology, which would contribute to a better supply of electricity and a more environmentally sustainable future.

Passive design strategies contribute to improving indoor comfort conditions and reducing buildings' energy consumption. For several years, courtyards have received wide attention from researchers because of their significant role in reducing energy demand. However, the abundance of multi-story buildings and the courtyards' incompatibility with them, the courtyard is currently limited. Therefore, it is necessary to search for alternatives. This paper aims to bridge the gaps in previous limited studies considering skycourt as a passive alternative on the vertical plane of the facades in contrast to the courtyard.

This research presents an overview and a bibliometric analysis of the evolution of the courtyard to the skycourt via VOSviewer software and the bibliometrix R package.

The research provided various concepts related to skycourt as a promising passive design strategy, which can be suitable for multi-story buildings, starting with its evolution, characteristics, configurations, benefits, and challenges.

The findings can urge designers, researchers and policymakers to incorporate such an important passive alternative.

Researchers, instructors, educational specialists, faculty members, and decision-makers can provide design motivation for skycourt in buildings, in addition to achieving awareness about skycourt and its significant benefits and its role as an important passive design strategy.

The research highlights the possibilities of the skycourt and its role as a passive design element as an extension of the courtyard in addition to identifying design indicators that help designers determine the appropriate designs.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.